Judgment and Decision-Making

Judgment and decision making (JDM) refers to an interdisciplinary area of research that seeks to determine how people make judgments and choices. The field considers perspectives from psychology, sociology, and economics; JDM researchers are found in psychology, management, economics, and marketing departments, as well as in schools of medicine, engineering, and public health. As this section is concentrated on industrial/organizational psychology, we adopt a mostly descriptive (i.e., psychological) perspective in discussing this topic. Psychologists have been concerned mostly with how people actually make decisions, whereas researchers from other areas (e.g., economics) have been concerned mostly with the rules that people should follow when making choices.

Expected Utility Theories

In general, decisions can be categorized depending on whether the outcomes of the available options are known for sure (decisions under certainty) or whether the outcomes are uncertain and occur with known or uncertain probabilities (decisions under uncertainty). Most research has focused on decisions under uncertainty, because such decisions are more common. Traditional theories of choice under uncertainty, such as subjective expected utility theory (SEUT), posit that choices are derived from only two parameters: (a) the subjective value, or utility, of an option’s outcomes and (b) the estimated probability of the outcomes. By multiplying the utilities with the associated probabilities and summing over all consequences, an expected utility is calculated. The option with the highest expected utility is then chosen.

Decision Analysis

This rational model of decision making has been used as a guide to study actual decision behavior and as a prescription to help individuals make better decisions. Multiattribute utility theory (MAUT) is a type of expected utility theory that has been especially influential in decision analysts’ attempts to improve organizational and individual decision making. Using MAUT, decision makers carefully analyze each option for its important attributes. For example, a job could be characterized by attributes such as salary, chances for promotion, and location. Decision weights are assigned to attributes according to their importance to the decision maker. Then, each available option is assessed for its expected value on all attributes. The values are then multiplied by the decision weights and summed, and the option with the highest value is selected.

Decision analysts have also developed various decision aids to help individuals and organizations make better decisions. Many of these aids rely heavily on modern information and communication technology, such as management information systems, expert systems, and artificial intelligence. Because few empirical evaluations of the various decision aids have been undertaken, claims about their effectiveness are mostly based more on logical argument than on research and should be considered speculative.

Prospect Theory

Theories of expected utility, such as SEUT, impress through their simplicity, generality, and rational appeal. However, they also place heavy demands on decision makers’ knowledge and cognitive abilities and neglect important aspects of the decision process, such as the search and interpretation of information. Under the heuristics and biases approach, JDM researchers have explored various ways in which decision makers deviate from rationality. The most important result of this research program is prospect theory.

Prospect theory (PT) was developed as a descriptive theory of decision making. Prospect theory uses a multiplicative model similar to the one used by expected utility theories. However, instead of utilities and probabilities, PT proposes that decision makers use certain value functions and decision weight functions. The decision weight function differs from a probability function in that low probabilities are overweighted and high probabilities are underweighted. The value function also differs from a typical utility function. Specifically, PT assumes that values are defined relative to a reference point (or the status quo). Further, PT posits that the value function is steeper in the domain of losses (below the reference point) than gains. Finally, the value function is concave above, and convex below, the reference point. This implies that decision makers are risk-averse in the gain domain but risk-seeking in the domain of losses.

Emotions and Motivation in Decision Making

A limitation of PT is the heavy emphasis on cognitive and psychophysical aspects of decision making. Common experience suggests that making choices can involve intense emotions. This is reflected in research on regret aversion. When making decisions, people often worry about the possibility of experiencing regret as a result of choosing an inferior option. Recent research suggests that such worries can even lead to better decision making by motivating people to engage in more vigilant information search and deliberation before making a choice. Other research suggests that positive affect can have beneficial effects on decision making—for example, by increasing creativity.

A related branch of decision-making research deals with flawed decision behavior when the optimal choice is readily apparent. In such situations, the decision maker is actually motivated to choose the option that is destructive in the long term. Decision-making researchers have identified this as an intrapersonal conflict, between what one wants to do and what one knows one should do. There are many nonwork examples of such decisions, such as when students elect to attend a party rather than study for an upcoming exam, or when cigarette smokers attempting to quit, despite their best intentions, accept a cigarette offered to them at a party. Such decisions are considered nonoptimal when, considered retrospectively, they lead to regret because they are inconsistent with decision makers’ long-term goals. There are many explanations for why individuals give in to their “want selves,” such as dispositional self-control and allowing themselves to reason through unwise promises (e.g., running up one’s credit card today while promising to stop next month).

Another explanation for these effects is time discounting. For example, consider when an honest employee has decided to confront a needy coworker who has been stealing from the company to make ends meet and provide for his family. The employee knows that it is in the best interest of the organization in the long term to confront the employee and ask him to curtail his behavior. However, these plans are destroyed when the dishonest but very likable coworker walks into the break room and the topic discussed is not the coworker’s theft, but mundane, friendly topics such as work and the weather. In this case, the long-term goals of unit performance and management trust are devalued relative to the more immediate feelings of comfort and positive emotional reactions by the coworker. Over time, such decisions can have negative effects on individual and organizational performance.

Individual Differences and Decision Making

As compared with industrial/organizational (I/O) psychology’s long-standing interest in individual differences, the interests of JDM researchers so far have had less to do with individual differences in decision making and their relation to decision quality and outcomes. An exception to this is the decision-making styles scales, which were developed to measure the degree to which decision makers report intuitive, dependent, rational, avoidant, and impulsive decision making. One recent study adapted the rational and intuitive items to job search and found that self-reported rational and intuitive strategies were related to job satisfaction and satisfaction with the job search process. Given that decision making is an important dimension of most managerial jobs, understanding the relation between decision making styles and managerial performance could be an important addition to the employee selection literature. Organizations might consider adding measures of decision making styles to selection batteries or adding decision-making style training for managers.

Fairness in Decision Making

Most decisions, especially in organizations, affect not only oneself, but others, as well. This raises questions of how decision outcomes should be distributed across parties. Research on organizational justice is concerned with the characteristics of decision processes and distributions of decision outcomes that lead to higher or lower levels of perceived fairness. Because there are articles on this site that describe the research on organizational justice perceptions extensively, here we concentrate only on the tension between what is rational and what is perceived as fair. From the standpoint of economics, decisions that maximize utility or profit should be preferred by decision makers. However, research shows that often individuals will sacrifice personal rewards to punish (a) someone who has acted unfairly toward them or (b) an individual who has wronged someone else when they have no relationship with either party. This speaks against pure self-interest and value maximization.

Consider a so-called ultimatum game in which someone is willing to split $100 between two parties provided that they can agree on how to split the money. One person is to make a decision on how to distribute the money, whereas the other person only has to decide on whether to accept the decision, or no money is distributed. Assume one party decides to take $98 of the money and give $2 to the other party. A value-maximizing decision maker should accept this decision. However, the majority of people playing this game reject this unfair distribution. Although such ultimatum games clearly have workplace applications, virtually none of this research has made it out of the laboratory and into workplace settings. We believe that self-sacrificial decision making to punish perceived unfairness in the workplace is an area for potentially interesting future research.

Applications to Job Choice and Employee Selection

Perhaps the areas in which JDM has contributed the most to I/O psychology are in job choice and employee selection. In the area of job choice, perhaps the largest area of cross-fertilization between JDM and I/O psychology is the application of the policy-capturing (PC) methodology to study job attribute preferences. Briefly, with the PC methodology, participants rate job-choice scenarios that differ on multiple job attributes. Participants provide some indication of organizational attractiveness on a Likert-type scale; then, using multiple regression, the researcher can determine for each individual the unique influence of each attribute on attractiveness ratings. Although the PC methodology also suffers from some limitations, it has allowed us to draw some conclusions about which job attributes are most important. These include opportunities for advancement, social status and prestige, responsibility, opportunities to use one’s skills, challenging work, opportunities to be creative, and high salary.

In both employee selection and job choice contexts, some I/O researchers have sought to determine whether phenomena observed in other decision-making contexts (e.g., consumer purchases) are also observed when the choice is among hypothetical job candidates or hypothetical job offers. Phenomena studied in selection contexts include order effects, attribute-range effects (i.e., the effects of high versus low variance on salary in job choice), attribute-salience effects (i.e., the effect of unique favorable attributes versus unique unfavorable attributes in options that are similar in overall attractiveness), and decoy effects (i.e., when the manipulation of the characteristics of an inferior option causes preference to shift between two superior options). In general, the published studies indicate that these effects are generalizable to job choice and employee selection contexts; however, when efforts have been made to make the decision context more similar to real-world decision making (e.g., when attributes are presented in text form or without numerical values), the effects tend to be weaker.

Conclusion

The fields of decision making and I/O psychology have much to offer to each other. However, I/O researchers have not to this point taken full advantage of JDM research. Many decision-making phenomena observed in the laboratory are potentially relevant to organizations but have not been studied in organizational contexts. Likewise, JDM researchers could learn from I/O psychology’s sophistication in such areas as field methodology and test construction. More communication between JDM and I/O researchers in the future can benefit both fields.